System and method for camera or sensor-based parking spot detection and identification

ABSTRACT

An on-board vehicle system and method for camera or sensor-based parking spot detection and identification is provided. This system and method utilizes a standard front (or side or rear) camera or sensor image to detect and identify one or more parking spots at a distance via vector or like representation using a deep neural network trained with data annotated using an annotation tool, without first transforming the standard camera or sensor image(s) to a bird&#39;s-eye-view (BEV) or the like. The system and method can be incorporated in a driver-assist (DA) or autonomous driving (AD) system.

CROSS-REFERENCE TO RELATED APPLICATION

The present disclosure is a continuation (CON) of co-pending U.S. patentapplication Ser. No. 16/129,871, filed on Sep. 13, 2018, and entitled“SYSTEM AND METHOD FOR CAMERA OR SENSOR-BASED PARKING SPOT DETECTION ANDIDENTIFICATION,” the contents of which are incorporated in full byreference herein.

TECHNICAL FIELD

The present disclosure relates generally to the automotive field. Morespecifically, the present disclosure relates to an on-board vehiclesystem and method for camera or sensor-based parking spot detection andidentification. This system and method utilizes a front (or side orrear) camera or sensor image to detect and identify one or more parkingspots at a distance via vector or like representation using a deepneural network trained with data annotated using an annotation tool,without first transforming the camera or sensor image(s) to abird's-eye-view (BEV) or the like. The system and method can beincorporated in a driver-assist (DA) or autonomous driving (AD) system.

BACKGROUND

A variety of conventional on-board vehicle parking spot detectionsystems are known to those of ordinary skill in the art. Most of theseparking spot detection systems utilize one or more proximity sensors,e.g. ultra-sonic sensors or the like, mounted on a vehicle to detect anempty parking spot between two occupied parking spots. Such detection islimited by the close range of operation of such sensors, typically onthe order of a few meters. Further, such detection requires the presenceof structures or obstacles, e.g. other vehicles, on either side of anempty parking spot to be detected. If an empty parking “slot” is notcreated by physical references, then detection fails. This limits theusefulness of these proximity sensor-based parking spot detectionsystems, even when coupled with various relatively slow, sub-optimal,and potentially unsafe planning-based automatic parking systems thatprovide maps of potential and/or available parking spots through thecloud or other vehicle-to-vehicle communication means. This limits theusefulness of the proximity sensor-based parking spot detection systemsin DA and AD systems.

Parking spot detection systems that utilize BEV camera images generatethe BEV from multiple, e.g. four, fisheye camera images that are warpedto be parallel to the ground and stitched together to create a view of avehicle from above, including the nearby surroundings. Lines andobstacles related to parking spots can then be segmented from these BEVcamera images. Again, however, such detection is limited in terms ofrange, typically to a few meters, and the BEV camera images tend to beundesirably distorted. This limits the usefulness of these BEV cameraimage-based parking spot detection systems in DA and AD systems.

Thus, what is still needed in the art is a parking spot detection systemthat utilizes a standard front (or side or rear) camera or sensor image,such that parking spot detection and identification is enhanced and maybe carried out at a distance, whether the parking spot is empty, full,surrounded by full parking spots, surrounded by empty parking spots,etc. Such a parking spot detection system is provided by the presentdisclosure.

SUMMARY

In various aspects, the present disclosure provides an on-board vehiclesystem and method for camera or sensor-based parking spot detection andidentification. This system and method utilizes a front (or side orrear) camera or sensor image to detect and identify one or more parkingspots at a distance via vector or like representation using a deepneural network trained with data annotated using an annotation tool,without first transforming the camera or sensor image(s) to a BEV or thelike. The system and method can form an integral part of a DA or ADsystem.

The vector or like representation of the present disclosure is a compactrepresentation that is encoded with the position, size, and orientationof a detected parking spot or spots, as well as entrance direction andtype identification (vacant, occupied, handicapped, emergency, loadingzone, etc.). It will be apparent to those of ordinary skill in the artthat such vector or like representation can be readily extended tospaces other than parking spots.

The deep neural network of the present disclosure used to detect andidentify parking spots from standard camera or sensor images andgenerate vectors or the like can, broadly to narrowly, be classified asan artificial intelligence (AI) network, a machine learning (ML)network, a deep learning (DL) network, a deep neural network (DNN), aconvolutional neural network (CNN), etc. The input to the network is thecamera or sensor image (or images) from the vehicle camera or sensor (orcameras or sensors), and the output is encoded vector(s) or the likerepresenting detected and identified parking spot(s).

The deep neural network is trained using a plurality of annotatedtraining images generated using an annotation tool. This annotation toolallows an annotator to select points on the training images,corresponding to parking spot points of interest, segment the trainingimages, annotate the training images, and save the results as a jsonfile or the like.

The base algorithm of the present disclosure is primarily focused on thelocal correctness of individual vectors or the like. An extendedalgorithm may be implemented that adds global awareness to the basealgorithm. This extended algorithm ensures that all generated vectors orthe like are smooth and globally consistent, just as the underlyingparking spots are smooth and globally consistent. In other words, thevectors or the like meet at consistent points, with consistent lines andangles, thereby more accurately representing the underlying physicalreality. The extended algorithm may utilize a generative approach, forexample, the family of variational autoencoders (VAE), or the family ofGenerative Adversarial Networks (GAN, cGAN, DCGAN, WGAN, etc.).

As the detected and identified parking spots are located atreadily-ascertainable image coordinates, they can be easily mapped to aBEV or the like, providing ground coordinates, as necessary, for DA orAD applications. These image coordinates can also be overlaid on anyvariety of camera or sensor images to provide an augmented reality toolfor assisting a driver in finding vacant parking spots, for example.

In one specific aspect, the present disclosure provides a system fordetecting and identifying a parking spot, including: a camera or sensoroperable for obtaining an image including a parking spot, wherein thecamera or sensor includes one or more of a front camera or sensor, aside camera or sensor, and a rear camera or sensor of a vehicle, andwherein the image includes one or more of a front image, a side image,and a rear image; and a processor executing an algorithm operable forgenerating a vector representation of the parking spot, wherein thevector representation includes information related to one or more of alocation, a size, an orientation, and a classification of the parkingspot. Optionally, the camera or sensor includes multiple of the frontcamera or sensor, the side camera or sensor and the rear camera orsensor, and wherein the image includes multiple of the front image, theside image, and the rear image stitched together. The processor isdisposed one of on-board the vehicle and remote from the vehicle in anetwork cloud. The algorithm is operable for executing an imagepre-processing stage, a network operations stage, and a post-processingstage that comprises a decoding step that interprets output of thenetwork operations stage and a vector-based non-maximum suppressionstep. The network is an artificial intelligence network trained using aplurality of training images that are annotated using an annotationtool. The annotation tool is operable for receiving selected points ofinterest on the plurality of images from an annotator, segmenting thetraining images based on the selected points of interest, annotating theplurality of training images, and saving the results as a json file orthe like, wherein the selected points of interest utilize one or moreclasses of markers, and wherein the plurality of training images areannotated using one or more classes of representations based on the oneor more classes of markers. Optionally, the algorithm utilizes agenerative algorithm operable for modifying the vector representationsuch that it is consistent with surrounding vector representationsgenerated from the image. The classification of the parking spotincludes one or more of unoccupied, occupied, and parking spot type.Optionally, the system further includes a display operable fordisplaying the vector representation to a driver of the vehicle overlaidon one of the image and another image. Optionally, the vectorrepresentation is communicated to and used by one or more of adriver-assist system and an autonomous driving system of the vehicle tomaneuver the vehicle into the parking spot when the vectorrepresentation indicates that the parking spot is unoccupied. The cameraor sensor is operable for obtaining the image at least 30 meters and upto 50 meters or more away. Optionally, the system is used in conjunctionwith one or more other sensors or systems of the vehicle operable forassessing the position of the vehicle in the surrounding environment.

In another specific aspect, the present disclosure provides a method fordetecting and identifying a parking spot, including: obtaining an imageincluding a parking spot using a camera or sensor, wherein the camera orsensor includes one or more of a front camera or sensor, a side cameraor sensor, and a rear camera or sensor of a vehicle, and wherein theimage includes one or more of a front image, a side image, and a rearimage; and generating a vector representation of the parking spot usinga processor executing an algorithm, wherein the vector representationincludes information related to one or more of a location, a size, anorientation, and a classification of the parking spot. Optionally, thecamera or sensor includes multiple of the front camera or sensor, theside camera or sensor and the rear camera or sensor, and wherein theimage includes multiple of the front image, the side image, and the rearimage stitched together. The processor is disposed one of on-board thevehicle and remote from the vehicle in a network cloud. The algorithm isoperable for executing an image pre-processing stage, a networkoperations stage, and a post-processing stage that comprises a decodingstep that interprets output of the network operations stage and avector-based non-maximum suppression step. The network is an artificialintelligence network trained using a plurality of training images thatare annotated using an annotation tool. The annotation tool is operablefor receiving selected points of interest on the plurality of imagesfrom an annotator, segmenting the training images based on the selectedpoints of interest, annotating the plurality of training images, andsaving the results as a json file or the like, wherein the selectedpoints of interest utilize one or more classes of markers, and whereinthe plurality of training images are annotated using one or more classesof representations based on the one or more classes of markers.Optionally, the algorithm utilizes a generative algorithm operable formodifying the vector representation such that it is consistent withsurrounding vector representations generated from the image. Theclassification of the parking spot includes one or more of unoccupied,occupied, and parking spot type. Optionally, the method further includesdisplaying the vector representation to a driver of the vehicle overlaidon one of the image and another image using a display. Optionally, thevector representation is communicated to and used by one or more of adriver-assist system and an autonomous driving system of the vehicle tomaneuver the vehicle into the parking spot when the vectorrepresentation indicates that the parking spot is unoccupied. The cameraor sensor is operable for obtaining the image at least 30 meters and upto 50 meters or more away. Optionally, the method is used in conjunctionwith one or more other sensors or systems of the vehicle operable forassessing the position of the vehicle in the surrounding environment.

In a further specific aspect, the present disclosure provides a vehicleincluding a system for detecting and identifying a parking spot, thevehicle including: a camera or sensor operable for obtaining an imageincluding a parking spot, wherein the camera or sensor includes one ormore of a front camera or sensor, a side camera or sensor, and a rearcamera or sensor of a vehicle, and wherein the image includes one ormore of a front image, a side image, and a rear image; one of anon-board processor and a communications link to a remote processorexecuting an algorithm operable for generating a vector representationof the parking spot, wherein the vector representation includesinformation related to one or more of a location, a size, anorientation, and a classification of the parking spot; and a displayoperable for displaying the vector representation to a driver of thevehicle overlaid on one of the image and another image. Optionally, thecamera or sensor includes multiple of the front camera or sensor, theside camera or sensor and the rear camera or sensor, and wherein theimage includes multiple of the front image, the side image, and the rearimage stitched together. The algorithm includes an artificialintelligence network trained using a plurality of training images thatare annotated using an annotation tool, and, optionally, the algorithmutilizes a generative algorithm operable for modifying the vectorrepresentation such that it is consistent with surrounding vectorrepresentations generated from the image. Optionally, the vehiclefurther includes one or more of a driver-assist system and an autonomousdriving system operable for receiving the vector representation andmaneuvering the vehicle into the parking spot when the vectorrepresentation indicates that the parking spot is unoccupied.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated and described herein withreference to the various drawings, in which like reference numbers areused to denote like system components/method steps, as appropriate, andin which:

FIG. 1 is a front camera image illustrating the parking spot vectorrepresentation of the present disclosure;

FIG. 2 is a schematic diagram illustrating the directionality of theparking spot vector representation of the present disclosure;

FIG. 3 is a schematic diagram illustrating one exemplary convention fororienting the parking spot vector representation of the presentdisclosure;

FIG. 4 is a schematic diagram illustrating one exemplary embodiment ofthe DNN of the present disclosure;

FIG. 5 is a table illustrating one exemplary network structure of theDNN of the present disclosure;

FIG. 6 is a schematic diagram illustrating the operation of the enhancedDNN of the present disclosure;

FIG. 7 is a schematic diagram illustrating one exemplary embodiment ofthe enhanced DNN of the present disclosure;

FIG. 8 is a series of front camera images illustrating the use of theannotation tool of the present disclosure; and

FIG. 9 is a series of images illustrating an overlay of the parking spotvector representation of the present disclosure on a BEV image or thelike, such that a driver-assist function may be provided.

DESCRIPTION OF EMBODIMENTS

Again, in various aspects, the present disclosure provides an on-boardvehicle system and method for camera or sensor-based parking spotdetection and identification. This system and method utilizes a front(or side or rear) camera or sensor image to detect and identify one ormore parking spots at a distance via vector representation using a deepneural network trained with data annotated using an annotation tool,without first transforming the camera or sensor image(s) to a BEV or thelike. The system and method can form an integral part of a DA or ADsystem.

The vector representation of the present disclosure is a compactrepresentation that is encoded with the position, size, and orientationof a detected parking spot or spots, as well as entrance direction andtype identification (vacant, occupied, handicapped, emergency, loadingzone, etc.). It will be apparent to those of ordinary skill in the artthat such vector representation can be any time of directionalrepresentation and can be readily extended to defined spaces other thanparking spots.

Referring now specifically to FIG. 1, a standard front vehicle cameraimage 10 is shown, with overlaid vector representations 12 of eachparking spot 14 provided. In general, these vector representations 12connect the points 16 associated with each front corner 18 of thecorresponding parking spot 14, highlighting the entrance 20 of eachparking spot 14. A first type of parking spot 14 a, such as anunoccupied parking spot, a standard parking spot, etc., may be indicatedby a first color or texture vector representation 12 a, while a secondtype of parking spot 14 b, such as an occupied parking spot, ahandicapped/emergency parking spot, etc., may be indicated by a secondcolor or texture vector representation 12 b. Thus, the vectorrepresentations 12 are encoded with classification information relatedto the parking spots 14, in addition to position, size, and orientationinformation. In this exemplary embodiment, the directional arroworientation of each vector representation 12 indicates which side of thevehicle 5 the entrance 20 of the corresponding parking spot 14 ispresent on, with generally upward-oriented arrows indicating an entrance20 on the left side of the vehicle 5 from the driver's perspective andgenerally downward-oriented arrows indicating an entrance 20 on theright side of the vehicle 5 from the driver's perspective. This is shownin FIG. 2. As shown in FIG. 3, 90-degree counter-clockwise rotation ofthe vector 12 points to the associated parking spot in the ground plane,for example, thereby providing a formally defined convention with vectorclassification encoding [0,1,0, . . . ,0] or vector regression encoding[x_(start),y_(start),x_(end),y_(end)], [x_(center),y_(center),l,Θ], or[x_(center),y_(center),l,sin Θ, cos Θ]. It will be readily apparent tothose of ordinary skill in the art that other representations may beused equally, provided that they are capable of communicating the sameor similar information.

As described herein above, most conventional on-board vehicle parkingspot detection systems (which the parking spot detection andidentification system of the present disclosure may complement) utilizeone or more proximity sensors, e.g. ultra-sonic sensors, radar sensors,or the like, mounted on a vehicle to detect an empty parking spotbetween two occupied parking spots. Such detection is limited by theclose range of operation of such sensors, typically on the order of afew meters. This is remedied by the parking spot detection andidentification system of the present disclosure, which can “see” aconsiderable distance in front of, next to, or behind the vehicle (onthe order of tens of meters). Thus, more parking spots can be “covered”per time unit, allowing for behavioral planning before a parking spothas been passed, for example. Further, such conventional detectionrequires the presence of structures or obstacles, e.g. other vehicles,on either side of an empty parking spot to be detected. If an emptyparking “slot” is not created by physical references, then detectionfails. This is again remedied by the parking spot detection andidentification system of the present disclosure, which is notconstrained by the presence of structures or obstacles on either side ofan empty parking spot to be detected. The parking spot detection andidentification system detects and identifies the parking spotsthemselves, in large part, from only a visible line or other marking.This enhances the usefulness of the parking spot detection andidentification system in DA and AD systems.

Similarly, conventional parking spot detection systems that utilize BEVcamera images generate the BEV from multiple, e.g. four, fisheye cameraimages that are warped to be parallel to the ground and stitchedtogether to create a view of a vehicle from above, including the nearbysurroundings. Lines and obstacles related to parking spots are segmentedfrom these BEV camera images. Such detection is limited in terms ofrange, typically to a few meters, and the BEV camera images aretypically undesirably distorted. This also limits the usefulness ofthese BEV camera image-based parking spot detection systems in DA and ADsystems. The parking spot detection and identification system of thepresent disclosure can “see” a considerable distance in front of, nextto, or behind the vehicle (on the order of tens of meters). Further, theuse of a front camera image takes full advantage of the presence of thevehicle headlights, in image acquisition at night, for example. This isnot the case when using a BEV image.

Referring now specifically to FIG. 4, the network 30 of the presentdisclosure used to detect and identify parking spots 14 (FIG. 1) fromstandard camera or sensor images 10 and generate vectors 12 can, broadlyto narrowly, be classified as an AI network, a ML network, a DL network,a DNN, a CNN, etc. The input to the network 30 is the camera or sensorimage (or images) 10 from the vehicle camera or sensor (or cameras orsensors), and the output is encoded vector(s) 12 representing detectedand identified parking spot(s) 14. The input image 10 is provided to abase CNN 32 or the like that creatures feature pyramids 34 includingmultiple levels 36 and anchor vectors 38 [p₀, . . . ,p_(n),t_(x),t_(y),t_(l), t_(Θ)], as multiple vectors are implicated by agiven parking spot 14. Classification and regression techniques 40 arethen utilized, and vector-based non-maximum suppression 42 is performedto achieve the final output vector representation 12 for the givenparking spot 14.

The whole pipeline can be divided into three stages: inputpre-processing, network operations, and output post-processing.

The input pre-processing stage includes grabbing frame(s) from thecamera(s) or sensor(s) and applying required input normalization toscale the pixel values to between −0.5 and 0.5 and provide zero (0) meanand unit (1) variance. The purpose of this stage is to allow for easiertraining of the following network 30 and to improve robustness ascompared to input noise.

The network operations stage takes the processed input image(s) 10 asinput and outputs the predicted vectors 12 encoded with classificationand regression information. The network structure can be further dividedinto three parts: feature extraction, the feature pyramids 34, andoutput heads. The feature extraction part is composed of the base CNN 32that is responsible for extracting useful features from the inputimage(s) 10. The feature pyramids 34 cast the extracted features intomulti-scale features to achieve scale robustness. The output headscontain a classification head and a regression head. The classificationhead outputs the class information of predicted vectors, and theregression head outputs the position, direction, and size of thepredicted vectors. For each layer in the feature pyramids 34, such pairof output heads is attached, which means that the prediction of vectorstakes place at different scales so that vectors of all sizes can bedetected and identified. The anchor vectors 38 are predefined vectorswith various orientations and lengths. When a vector is sufficientlysimilar to an anchor vector 38, the anchor vector 38 is activated andassigned a score based on similarity. During training of the network 30,the anchor vectors 38 are assigned positive (activated) or negative(deactivated) status based on their similarity score with ground truthvectors derived from annotation. The similarity between two vectors isdetermined by a combination of center position, length, and direction ofthe two vectors. When the similarity score is higher than a predefinedvalue, the anchor vectors 38 are given the label positive. When thesimilarity score is lower than a predefined value, the anchor vectors 38are given the label negative. Potentially, when the two values are setdifferently, the anchor vectors 38 with a similarity score in betweenwill be set to be ignored during the calculation of the loss. Thetraining process involves iteratively updating the value of theparameters of the network 30 so that the loss (a value characterizingthe prediction error) is small between the predicted vectors and theground-truth vectors derived from annotation. The outputs are encoded sothat each vector is a transformed version of an activated anchor vector38. The [p₀, . . . , p_(n)] encodes which class the vector belongs to.The [t_(x),t_(y),t_(l),t_(Θ)] encodes how the vector is transformed fromthe anchor vector 38 using the following formulas:

${t_{x} = \frac{b_{x} - a_{x}}{a_{l}}},{t_{y} = \frac{b_{y} - a_{y}}{a_{l}}},{t_{l} = {\log\left( \frac{b_{l}}{a_{l}} \right)}},{t_{\theta} = {b_{\theta} - a_{\theta}}},$where a and b represent the anchor vector 38 and the vector to beencoded, respectively; subscripts x, y, l, and θ represent thehorizontal and vertical coordinates of the center of the vector, thelength of the vector, and the direction of the vector, respectively.

The output post-processing stage includes a decoding step thatinterprets the output of the network operations stage and a vector-basednon-maximum suppression (NMS) step. The vector-based NMS step isspecifically designed to operate on vectors, as opposed to boundingboxes for standard NMS. To do so, each vector is augmented into a circle39 whose center is at the center of the vector (which is[x_(center),y_(center)]), and the diameter is the length of the vector(l). The intersection-over-union (IoU) score of the circles 39 is thencalculated to replace the IoU score used in a standard NMS. In practice,the circumscribed square of said circle 39 is used in place of thecircle 39, for faster computation with little quality loss.

FIG. 5 is a table illustrating one exemplary network structure 44 of theDNN 30 of the present disclosure. Feature extraction incorporates aResNet-like structure. Conv1, 2, . . . , 5 represents convolutionblocks. Each row in a convolution block contains the following layers,in sequence: 2D-convolution (Conv2D), batch normalization (BN), andrectified linear unit (ReLU). There are residual layers (i.e. skipconnections) between convolution blocks. The feature pyramid 34 (FIG. 4)has 5 levels 36 (FIG. 4), each carrying further extracted featureinformation at corresponding scales. A pair of classification andregression heads is attached to each level 36 of the feature pyramid 34.Here, k is the number of classes, a is the number of anchor vectors perposition, and d is the dimension of the vector regression encoding.Note, the network structure 44 may vary considerably, with this specificnetwork structure 44 being exemplary only.

Referring now specifically to FIG. 6, the base algorithm of the presentdisclosure is primarily focused on the local correctness of individualvectors 12 c. An extended algorithm may be implemented that adds globalawareness to the base algorithm, providing globally “smoothed” vectors12 d. This extended algorithm ensures that all generated vectors 12 dare globally consistent, just as the underlying parking spots 14(FIG. 1) are globally consistent. In other words, the vectors 12 d meetat consistent points, with consistent lines and angles, thereby moreaccurately representing the underlying physical reality. The extendedalgorithm may utilize a generative approach, for example, the family ofvariational autoencoders (VAE), or the family of Generative AdversarialNetworks (GAN, cGAN, DCGAN, WGAN, etc.), collectively the GAN 45 (FIG.7). The GAN 45 acts as a global constraint, and different types of GANs45 may be used to overcome the instability of training.

Referring now specifically to FIG. 7, the GAN 45 is a generative modelthat can produce realistic samples from random vectors drawn from aknown distribution. The GAN 45 consists of a generator 50 and adiscriminator 52, both of which are usually implemented as DNNs. Thetraining of the GAN 45 involves an adversarial game between thegenerator 50 and the discriminator 52. In this context, the generator 50creates vectors that are intended to come from the same distribution asthe vectors in the training data; the discriminator 52 tries to classifybetween vectors generated by the generator 50 (trying to assign score 0)and real vectors from the training data (trying to assign score 1).Thus, the network 30 (FIG. 4) now act as the generator 50 in the GANframework. The discriminator 52 learns to distinguish between thevectors predicted by the network 30 and the annotated ground-truthvectors in the training data. By doing so, the GAN framework tries toenforce its generator 50 (i.e. the network 30) to generate vectors asrealistic as the true vectors so that discriminator 52 is hard todistinguish. The loss function of the GAN 45 is binary cross entropy,and this loss is added to the original loss of the network 30 forback-propagation during training of the network 30. As shown in FIG. 6,in the beginning, the discriminator 52 (FIG. 7) will be able to tellthat the left vectors are generated because the real vectors usuallylook like the ones on the right. As the training goes on, the generator50 (FIG. 7) learns to generate vectors that are more realistic, and theylook more and more natural and consistent, like the ones on the right.During deployment phase, only the generator 50, which is “tuned” by theGAN 45, is deployed. Overall computation is only increased in thetraining phase, not when the trained model is actually used. Thus,on-board time consumption is not increased by the presence of the GAN45.

Referring now specifically to FIG. 8, the network 30 (FIG. 4) is trainedusing a plurality of annotated training images 60 b generated using anannotation tool 60. This annotation tool 60 allows an annotator toselect points 62 and 64 on the training images 60 a, corresponding toparking spot points of interest 66 and 68, segment the training images60 a, annotate the training images 60 a, and save the results to a jsonfile 70 or the like. In this specific example, a training image 60 a isannotated with two vector classes, although it will be readily apparentto those of ordinary skill in the art that more could be utilizedequally. First, entrance points 66 of various parking spots 14 areselected using a first class of markers 62 indicative of a first parkingspot characteristic or characteristics (e.g. unoccupied, standard,etc.). Second, entrance points 68 of various parking spots 14 areselected using a second class of markers 64 indicative of a secondparking spot characteristic or characteristics (e.g. occupied,handicapped, emergency, etc.). Third, encoded vector representations 12are generated using the markers 62 and 64 and the training image 60 b issaved as the json file 70 or the like for later use.

Referring now specifically to FIG. 9, as the detected and identifiedparking spots 14 are located at readily-ascertainable image coordinates,they can be easily mapped to a BEV 80 or the like, providing groundcoordinates, as necessary, for DA or AD applications. These imagecoordinates can also be overlaid on any variety of camera images toprovide an augmented reality tool for assisting a driver in findingvacant parking spots 14, for example.

Preferably, the software application/algorithm of the present disclosureis implemented as coded instructions stored in a memory and executed bya processor. The processor is a hardware device for executing such codedinstructions. The processor can be any custom made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the memory, asemiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing coded instructions. Theprocessor is configured to execute software stored within the memory, tocommunicate data to and from the memory, and to generally controloperations pursuant to the coded instructions. In an exemplaryembodiment, the processor may include a mobile optimized processor, suchas one optimized for power consumption and mobile applications.Input/output (I/O) interfaces can be used to receive user input and/orfor providing system output. User input can be provided via, forexample, a keypad, a touch screen, a scroll ball, a scroll bar, buttons,a voice-activation system, and/or the like. System output can beprovided via a display device, such as a liquid crystal display (LCD),touch screen, and/or the like. The I/O interfaces can also include, forexample, a serial port, a parallel port, a small computer systeminterface (SCSI), an infrared (IR) interface, a radio frequency (RF)interface, a universal serial bus (USB) interface, and/or the like. TheI/O interfaces can include a graphical user interface (GUI) that enablesthe user to interact with the memory. Additionally, the I/O interfacesmay further include an imaging device, i.e. the camera, a video camera,a sensor, etc.

The memory may include any of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatilememory elements (e.g., ROM, hard drive, etc.), and combinations thereof.Moreover, the memory may incorporate electronic, magnetic, optical,and/or other types of storage media. Note that the memory may have adistributed architecture, where various components are situated remotelyfrom one another, but can be accessed by the processor. The software inmemory can include one or more software programs, each of which includesan ordered listing of executable instructions for implementing logicalfunctions. The software in the memory includes a suitable operatingsystem (O/S) and programs. The operating system essentially controls theexecution of other computer programs, and provides scheduling,input-output control, file and data management, memory management, andcommunication control and related services. The programs may includevarious applications, add-ons, etc. configured to provide end userfunctionality. The programs can include an application or “app” whichprovides various functionalities.

Thus, in various aspects, the present disclosure provides an on-boardvehicle system and method for camera or sensor-based parking spotdetection and identification. This system and method utilizes a front(or side or rear) camera or sensor image to detect and identify one ormore parking spots at a distance via vector representation using a deepneural network trained with data annotated using an annotation tool,without first transforming the camera or sensor image(s) to a BEV or thelike. The system and method can be incorporated in a DA or AD system,providing the DA or AD system with its perception capability. The systemand method can be integrated with conventional parking solutions,including proximity sensors and near-field BEV methodologies. Parkingspot information can be cloud-shared as parking lots maps, etc. It canalso be used by businesses and the like to assess capacity utilization,etc.

The vector representation of the present disclosure is a compactrepresentation that is encoded with the position, size, and orientationof a detected parking spot or spots, as well as entrance direction andtype identification (vacant, occupied, handicapped, emergency, loadingzone, etc.). It will be readily apparent to those of ordinary skill inthe art that such vector representation can be extended to other thanparking spots equally.

The present disclosure has a number of exemplary applications. Relatedto driver assistance, long-range parking spot detection is provided inthe form of user interface, augmented reality (UIAR). Related toautonomous driving, a vehicle can find vacant parking spots at adistance and find its way to the spots autonomously, then complete theparking process. Related to parking lot and garage mapping, with thelarge coverage enabled by the front camera or sensor, a vehicle equippedwith this system can quickly map the parking spot configuration of aparking lot or garage, including the number of parking spots, parkingtype distribution (e.g. percentage of handicapped parking), floorlayout, etc. Such information would prove valuable for mapping companiesand the like. Related to the cloud sharing of parking information, forall vehicles with this system installed, they could upload the detectedparking spots, either occupied or vacant, with relevant information,such as types of parking spots, positions, sizes, and orientations ofparking spots, etc. Such information could be shared among all nearbyvehicles to help them locate available parking spots. Such informationwould also be valuable for third-party applications that provide parkinginformation. Related to business information collection, this systemcould quickly collect the usage rate of a parking lot or garage, andevaluate the business activity level based on the percentage of parkedvehicles, type of parked vehicles, etc. The system could also be coupledwith license plate detection in order to mine further detailedinformation. Such information would be valuable to consulting companiesand the like.

Although the present disclosure is illustrated and described herein withreference to preferred embodiments and specific examples thereof, itwill be readily apparent to those of ordinary skill in the art thatother embodiments and examples may perform similar functions and/orachieve like results. All such equivalent embodiments and examples arewithin the spirit and scope of the present disclosure, are contemplatedthereby, and are intended to be covered by the following non-limitingclaims for all purposes.

What is claimed is:
 1. A system, comprising: a camera or sensor operablefor obtaining an image encompassing a defined space, wherein the cameraor sensor comprises one or more of a front camera or sensor, a sidecamera or sensor, and a rear camera or sensor mounted on a vehicle, andwherein the image comprises one or more of a front image, a side image,and a rear image showing a corresponding view from the a vehicle; and aprocessor executing an algorithm operable for segmenting the definedspace in the image and overlaying a directional representationconnecting points associated with the defined space, wherein thedirectional representation comprises information related to one or moreof a location, a size, and an orientation of the defined space, andwherein the directional representation comprises information related toa classification of the defined space.
 2. The system of claim 1, whereinthe camera or sensor comprises multiple of the front camera or sensor,the side camera or sensor and the rear camera or sensor, and wherein theimage comprises multiple of the front image, the side image, and therear image stitched together.
 3. The system of claim 1, wherein theprocessor is disposed one of on-board the vehicle and remote from thevehicle in a network cloud.
 4. The system of claim 1, wherein thealgorithm is operable for executing a stage comprising one or more of animage pre-processing stage, a network operations stage, and apost-processing stage that comprises a decoding step that interpretsoutput of the network operations stage and a non-maximum suppressionstep, and wherein the network comprises an artificial intelligencenetwork trained using a plurality of training images that are annotatedusing an annotation tool.
 5. The system of claim 4, wherein theannotation tool is operable for receiving selected points of interest onthe plurality of images from an annotator, segmenting the trainingimages based on the selected points of interest, annotating theplurality of training images, and saving the results as a j son file orthe like, wherein the selected points of interest utilize one or moreclasses of markers, and wherein the plurality of training images areannotated using one or more classes of representations based on the oneor more classes of markers.
 6. The system of claim 1, wherein thealgorithm utilizes a generative algorithm operable for modifying thedirectional representation such that it is consistent with surroundingdirectional representations generated from the image.
 7. The system ofclaim 1, wherein the classification of the defined space comprises oneor more of unoccupied, occupied, and defined space type.
 8. The systemof claim 1, further comprising a display operable for displaying thedirectional representation to a driver of the vehicle overlaid on one ofthe image and another image.
 9. The system of claim 1, wherein thedirectional representation is one or more of: communicated to and usedby one or more of a driver-assist system and an autonomous drivingsystem of the vehicle to maneuver the vehicle into the defined spacewhen the directional representation indicates that the defined space isunoccupied, communicated to a cloud network and shared with othervehicles, and used to generate a map of an area.
 10. A method,comprising: obtaining an image encompassing a defined space using acamera or sensor, wherein the camera or sensor comprises one or more ofa front camera or sensor, a side camera or sensor, and a rear camera orsensor mounted on a vehicle, and wherein the image comprises one or moreof a front image, a side image, and a rear image showing a correspondingview from a vehicle; and segmenting the defined space in the image andoverlaying a directional representation connecting points associatedwith the defined space using a processor executing an algorithm, whereinthe directional representation comprises information related to one ormore of a location, a size, and an orientation of the defined space, andwherein the directional representation comprises information related toa classification of the defined space.
 11. The method of claim 10,wherein the camera or sensor comprises multiple of the front camera orsensor, the side camera or sensor and the rear camera or sensor, andwherein the image comprises multiple of the front image, the side image,and the rear image stitched together.
 12. The method of claim 10,wherein the processor is disposed one of on-board the vehicle and remotefrom the vehicle in a network cloud.
 13. The method of claim 10, whereinthe algorithm is operable for executing a stage comprising one or moreof an image pre-processing stage, a network operations stage, and apost-processing stage that comprises a decoding step that interpretsoutput of the network operations stage and a non-maximum suppressionstep, and wherein the network comprises an artificial intelligencenetwork trained using a plurality of training images that are annotatedusing an annotation tool.
 14. The method of claim 13, wherein theannotation tool is operable for receiving selected points of interest onthe plurality of images from an annotator, segmenting the trainingimages based on the selected points of interest, annotating theplurality of training images, and saving the results as a j son file orthe like, wherein the selected points of interest utilize one or moreclasses of markers, and wherein the plurality of training images areannotated using one or more classes of representations based on the oneor more classes of markers.
 15. The method of claim 10, wherein thealgorithm utilizes a generative algorithm operable for modifying thedirectional representation such that it is consistent with surroundingdirectional representations generated from the image.
 16. The method ofclaim 10, wherein the classification of the defined space comprises oneor more of unoccupied, occupied, and defined space type.
 17. The methodof claim 10, further comprising displaying the directionalrepresentation to a driver of the vehicle overlaid on one of the imageand another image using a display.
 18. The method of claim 10, whereinthe directional representation is one or more of: communicated to andused by one or more of a driver-assist system and an autonomous drivingsystem of the vehicle to maneuver the vehicle into the defined spacewhen the directional representation indicates that the defined space isunoccupied, communicated to a cloud network and shared with othervehicles, and used to generate a map of an area.
 19. A vehicle,comprising: a camera or sensor operable for obtaining an imageencompassing a defined space, wherein the camera or sensor comprises oneor more of a front camera or sensor, a side camera or sensor, and a rearcamera or sensor mounted on the vehicle, and wherein the image comprisesone or more of a front image, a side image, and a rear image showing acorresponding view from the vehicle; one of an on-board processor and acommunications link to a remote processor executing an algorithmoperable for segmenting the defined space in the image and overlaying adirectional representation connecting points associated with the definedspace, wherein the directional representation comprises informationrelated to one or more of a location, a size, and an orientation of thedefined space, and wherein the directional representation comprisesinformation related to a classification of the defined space; and adisplay operable for displaying the directional representation to adriver of the vehicle overlaid on one of the image and another image.20. The vehicle of claim 19, further comprising one or more of adriver-assist system and an autonomous driving system operable forreceiving the directional representation and maneuvering the vehicleinto the defined space when the directional representation indicatesthat the defined space is unoccupied.